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Chronic, cortex-wide imaging of specific cell populations during behavior

Nature Protocols volume 16, pages 3241–3263 (2021)Cite this article

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Abstract

Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Wide-field imaging of genetically encoded indicators is a high-throughput, cost-effective and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a wide-field macroscope setup, performing surgery to prepare the intact skull and imaging neural activity chronically in behaving, transgenic mice. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets laboratories that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging and/or analyze cortex-wide neuronal recordings. The entire protocol, including steps for assembly and calibration of the macroscope, surgical preparation, imaging and data analysis, requires a total of 8 h. It is designed to be accessible to laboratories with limited expertise in imaging methods or interest in high-throughput imaging during behavior.

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Fig. 1: Overview of the procedure described in this protocol.
Fig. 2: Acquisition in rolling shutter, dual color excitation mode and synchronization.
Fig. 3: Correction of hemodynamic artifacts with alternating violet and blue illumination.
Fig. 4: Detailed build instructions for the wide-field macroscope.
Fig. 5: Overview of the surgical procedures.
Fig. 6: Diagram of the data processing pipeline.
Fig. 7: Wide-field imaging applications.

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Data availability

The raw datasets used to generate the visual sign, stimuli triggered averages and linear regression analysis maps are available in a public repository, maintained by Cold Spring Harbor Laboratory with https://doi.org/10.14224/1.38599. Example datasets to test the analysis pipeline are at http://labshare.cshl.edu/shares/library/repository/38599/2021-01-20-Update/.

Code availability

Source code used in this protocol is available in the online repositories without access restrictions under a general public license at https://github.com/churchlandlab. The code and NeuroCAAS platform will remain available for the foreseeable future.

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Acknowledgements

We thank M. Kaufman and K. Odoemene for help with developing early versions of the protocol; P. Gupta, F. Albeanu and J. Wekselblatt for technical advice; N. Steinmetz, M. Pachitariu and K. Harris for help with wide-field analysis; and Z. Josh Huang for providing FezF2 mice. Financial support was received from the Swiss National Science foundation (S.M., grant no. P2ZHP3_161770), the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG - 368482240/GRK2416), the NIH (grant no. EY R01EY022979 and BRAIN initiative 5R01EB026949) and the Army Research Office under contract no. W911NF-16-1-0368 as part of the collaboration between the US DOD, the UK MOD and the UK Engineering and Physical Research Council under the Multidisciplinary University Research Initiative (A.K.C.). X.R.S. was supported by the NINDS BRAIN Initiative of the National Institutes of Health under award number F32MH120888. T.A. was supported by NIH training grant 2T32NS064929-11. S.S. was supported by the Swiss National Science Foundation P400P2 186759 and NIH 5U19NS104649. J.P.C. was supported by Simons 542963 and the McKnight Foundation. L.P. was funded by IARPA MICRONS D16PC00003, NIH 5U01NS103489, 5U19NS104649, 5U19NS107613, 1UF1NS107696, 1UF1NS108213, 1RF1MH120680, DARPA NESD N66001-17-C-4002 and Simons Foundation 543023. L.P. and J.P.C. were supported by NSF Neuronex Award DBI-1707398.

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Author notes

  1. These authors contributed equally: Joao Couto, Simon Musall.

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA

    Joao Couto, Xiaonan R. Sun, Anup Khanal, Steven Gluf & Anne K. Churchland

  2. Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA

    Joao Couto, Anup Khanal & Anne K. Churchland

  3. Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich, Jülich, Germany

    Simon Musall

  4. Department of Neurophysiology, Institute of Biology 2, RWTH Aachen University, Aachen, Germany

    Simon Musall

  5. Department of Neurosurgery, Zucker School of Medicine, Hofstra University, Hempstead, NY, USA

    Xiaonan R. Sun

  6. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Shreya Saxena, Taiga Abe, John P. Cunningham & Liam Paninski

  7. Department of Statistics, Columbia University, New York, NY, USA

    Shreya Saxena, Ian Kinsella, John P. Cunningham & Liam Paninski

  8. Center for Theoretical Neuroscience, Columbia University, New York, NY, USA

    Shreya Saxena, Ian Kinsella, Taiga Abe, John P. Cunningham & Liam Paninski

  9. Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA

    Shreya Saxena, Ian Kinsella, Taiga Abe, John P. Cunningham & Liam Paninski

  10. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

    Shreya Saxena

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Contributions

S.M. and A.K.C. conceptualized early versions of the procedures. S.M. and S.G. implemented early setup versions and acquisition workflow. S.M., X.R.S. and J.C. refined surgical procedures. All authors refined the macroscope building procedures and compiled the required part lists. S.M. and J.C. wrote acquisition software. S.M., X.R.S. and S.G. prepared animals and acquired data in the expected results. J.C., S.M., I.K. and S.S. wrote software for analysis. T.A., I.K., S.S., J.P.C. and L.P conceptualized the general analysis workflow. T.A., I.K. and S.S. deployed software on NeuroCAAS with input from J.C. and S.M.. A.K., J.C. and S.M. prepared figures. J.C., S.M., S.G., A.K. and A.K.C. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Anne K. Churchland.

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The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Ariel Gilad and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Key reference using this protocol

Musall, S. et al. Nat. Neurosci. 22, 1677–1686 (2019): https://doi.org/10.1038/s41593-019-0502-4

Extended data

Extended Data Fig. 1 Photobleaching due to repeated or high-power imaging.

a, Photochemical degradation of the fluorophore can occur over days because of repeated imaging. Decrease in fluorescence can be observed after 10 min when imaging at more than 50 mW of blue light (blue trace); signals are more stable at lower intensities (black, cyan traces). b, Top: images from a mouse expressing GCaMP6 in a subpopulation of cortical excitatory projection neurons. Visible decrease in overall fluorescence is evident after daily imaging at day 5 and 10. Bottom: histogram of pixel intensities corresponding to the images above.

Extended Data Fig. 2 Steps for setup calibration.

a, Alignment of the excitation dichroic. b, Alignment of the emission dichroic using Camware. c, Procedure for obtaining uniform illumination with the Camware software and the line profiler. The WidefieldImager also has a calibration mode with similar functionality and supporting cameras from multiple vendors.

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Couto, J., Musall, S., Sun, X.R. et al. Chronic, cortex-wide imaging of specific cell populations during behavior. Nat Protoc 16, 3241–3263 (2021). https://doi.org/10.1038/s41596-021-00527-z

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